I am interested in topics like nonlinear optimization algorithms, machine learning, and financial engineering. His ongoing paper is about constrained stochastic optimation with inequality constraints, and his previous projects include gradient sampling methods and optimal decision trees.

Papers:

  • Frank Curtis, Minhan Li. Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation. arXiv preprint arXiv:2005.07822. Under Review.
  • Oktay Gunluk, Jayant Kalagnanam, Minhan Li, Matt Menickelly, Katya Scheinberg. Optimal Decision Trees for Categorical Data via Integer Programming. arXiv preprint arXiv:1612.03225. Under Review.
  • Hiva Ghanbari, Minhan Li, and Katya Scheinberg. Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers. arXiv preprint arXiv:1903.00359, poster presentation at the NeurIPS 2019 workshop on “Beyond First Order Methods in Machine Learning”.
  • Krishnan Kumaran, Dimitri Papageorgiou, Yutong Chang, Minhan Li, and Martin Takac. Active Metric Learning for Supervised Classification. arXiv preprint arXiv:1803.10647